{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows multiple outputs from a single jupyter notebook cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in daily data for the S&P 500 from November of 2019 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>3050.72</td>\n",
       "      <td>3066.95</td>\n",
       "      <td>3050.72</td>\n",
       "      <td>3066.91</td>\n",
       "      <td>510301237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-04</th>\n",
       "      <td>3078.96</td>\n",
       "      <td>3085.20</td>\n",
       "      <td>3074.87</td>\n",
       "      <td>3078.27</td>\n",
       "      <td>524848878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05</th>\n",
       "      <td>3080.80</td>\n",
       "      <td>3083.95</td>\n",
       "      <td>3072.15</td>\n",
       "      <td>3074.62</td>\n",
       "      <td>585634570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close     Volume\n",
       "Date                                                     \n",
       "2019-11-01  3050.72  3066.95  3050.72  3066.91  510301237\n",
       "2019-11-04  3078.96  3085.20  3074.87  3078.27  524848878\n",
       "2019-11-05  3080.80  3083.95  3072.15  3074.62  585634570"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-11-27</th>\n",
       "      <td>3145.49</td>\n",
       "      <td>3154.26</td>\n",
       "      <td>3143.41</td>\n",
       "      <td>3153.63</td>\n",
       "      <td>421853938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-29</th>\n",
       "      <td>3147.18</td>\n",
       "      <td>3150.30</td>\n",
       "      <td>3139.34</td>\n",
       "      <td>3140.98</td>\n",
       "      <td>286602291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close     Volume\n",
       "Date                                                     \n",
       "2019-11-27  3145.49  3154.26  3143.41  3153.63  421853938\n",
       "2019-11-29  3147.18  3150.30  3139.34  3140.98  286602291"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily = pd.read_csv('../data/SP500_NOV2019_Hist.csv',index_col=0,parse_dates=True)\n",
    "daily.index.name = 'Date'\n",
    "daily.shape\n",
    "daily.head(3)\n",
    "daily.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PercentChange'] = (df['Close']/df['Open'] - 1.0)*100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7facd4df07d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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juYykAy29OB3C8kWZ/HqeQ3Bae4c40zc8p3hCOIuy03hfbTmP7WxK2ALJSDM8Ns6OUwurTwin3JORUMN2zFopbASOKaUalFIjwM+BG8MPUEqFl8NmAJb6BinOSafc40qoeoX2WdQozMS1qxZxz3Ur+O3eVr7xvLWuhvc397KsMJN31pSyr7mXY2fmvgoMn7Q2Xz52ZRVpyU4eeM5a58eu7Dndy/CYf9bzmGei3OOia2CEvgRpTWKWUSgBwieMNAW2vQYR+VsROY6xUvjEZE8kIneKSL2I1Hd0dERF7FRsWuJhW0Mn4wkSVwgZBffcWwSEc+cVldx8qZcHnjvKU3usMcReKcW+Zh+rS9xsqS7GIfDrXXPXtquxm9QkByuKsuatxZOZygc2V/DUnpaEbhUSKbYG4gkbFxhPCJJoGUhmGYXJKnou+GZVSn1bKVUF/APwucmeSCn1kFKqRilVU1BQEGGZ01Nb5cE3NJYwH9xgquZ83UdBRIQvvG01Gypy+fSje9gdmDsQS870DXO2f5jVxdkUZqexeWk+v97dPOdEgl2ne1hT4iZ5FnUc03Hn66vISkvi688eWdDzaAwX78rF2eS4Fh5PAKOqGbRRiDRNQGnYfS8w3WXZz4GboqpoHmxKsLnNbb4hMlKcU7aCngupSU7+8/ZLKchK5SP/U09rb2xz84OpsqtLjNkHb7+khKbuc9Sf6p71c4yM+dnX3DvveEI4blcyH3l9JX94tZ29TbE3mnZlaHScHY3d82qVPRXlnsBchQRpoW2WUdgOLBORJSKSAtwKPBl+gIgsC7v7FsByDtbF7jSW5GckTHO8dt/QBXMUFoInM5Xv37GBcyPjfPjhegZHYjdsZl9zLyJGMBzgjasWk57s5PE5BJwPtvoYGfPPO/NoIh/YXEGuK5mv/UGvFubL7tM9jIwtvD4hnMzUJPIzUxImbdgUo6CUGgPuAp4BDgKPKKUOiMh9IrIlcNhdInJARHYDdwN3mKFtrmyq9PDKia6EmNva1jvEoqzIGQWAixZn8R+3redgq4+7f7EnZnUf+5t9VBVk4kpJAiAjNYk3XbyI3+5tmfWI0WDRWiRWCgBZacl87Moq/vdIB9tPJl6hZCSoa4hsPCFIWQJ1SzWtTkEp9bRSarlSqkop9YXAtnuVUk8Gbn9SKXWxUmqdUuoqpdQBs7TNhdoqD33DY7yaAHGFdt/wvGoUZuKqFYV89i2r+P2BNu6PkQ99f3Mva0peOzbzpvUl+IbG+NOh2SUw7Drdw+LsNIrc6RHT9b7aCvIzU/naHw5H7DkTia3HO7m4OBt3+sJdnuEkUlqqrmieI5sCVyDx7kLy+5XhPlpgkHkqPri5gts2lvKtPx3j8V1NUXmNqejoG6bNN8TFAddRkNctzSc/M2XWNQszTVqbD+kpTu66qoq6hi5ePpZ4vbYWwtDoOLtO90QsFTWcsjwXLb3nZr2KtDPaKMyRwuw0qgoy4r5eoXNgJDCGc2HpqFMhIvzLltVsqszjH365jx1zCPAulAMtrw0yB0lyOrihupg/HjpD7+D0Oemd/cM0dg1G3CgA3HZZGcXuNP79D4ctV/BnZXY2dkc8nhCkIt+FUnC6K/6bF2qjMA9qqzxsj/O4wvkaheisFMDoK/Pgey6lKCeNj/6onqZuc5bnwcyjiSsFgLev9zIy7ufpGTq8BtNqIxVkDic1ycnHr17GzsYeXjhsbi2Onalr6MIhsCHC8QSAsrxAC+05ZiANjY4zMBy7hIr5oI3CPNhU6WFgZJx9Fu4AulDaF9jiYrbkZqTw/Ts2MDzm58MP19Nvwgdof7OPJfkZk6bari7Jpqogg8d3Tu9C2tXYQ5JDWF3snva4+XLzpV7K8lx6tTAH6ho6WV3iJjsCKdQTKZ9HrYLfr3jv97fx/v9+JeJ6ook2CvMgNLc5jl1IbSasFIIsLczk2+++hKNn+vm7n++OesX4/pbeSVcJYLi13ra+hFdOdnF6msDirtPdrCzKJj3FGRWNyU4Hn7x6GQdafDxzoC0qrxFPDI2Os7uxJyquIwBPRgoZKc45GYVf7mxi+8ludjX22GpErTYK8yA/M5XlizLjer5Ce68xhrMgMzoxhYlcsbyAf75hFc8dbOcrvz8UtdfpHhihqfvcBZlH4dy4zujA8uQULTnG/Yo9pyNTtDYdN60voaogg/ufPZIwrVXmy85T3YyMR67f0UREhDJPBqdm2UK7d3CUL//uEFmpSYz5la26K2ujME9qKz3Un+xiNE7jCm2+IfIzZzeGM1K8r7aC924q57t/buCR+tMzP2AeHGgxUoknBpnDKc1zsaEil8d2Nk3qujl2pp/+4bGoGwWnQ/jUtcs50t7Pb/Zao2eUVdna0IlDoKYi8jGeIBUe16yH7dz/7GG6B0e4/13rAGxVpa6NwjzZVOlhcGTcVv/sudAWpRqFmbj3hlW8bmk+n318H6+ciPxKbN80QeZwblpfwvGOgZARCSdUtLaAzqiz5c2ri1ixOIsHnjsa14kNC6WuoZM1Je6ItGSZijKPi6auczOu2g609PKjulPcvqmca1YW4slIYW+TfeKP2ijMk8tCc5vjM67Q3jtEYYSrmWdDstPBt999CaW5Lj76o/qItxbY39JLaV76jM3S3rqmmBSnY9K2F7sae8h1JYeCj9HE4RD+/o0XceLsAI/Nc+ZDvHNuZJzdp3vYFMF+R5NRnpfByLg/FG+bDKUU//zEAXJdKfz9tRchIqzxukMDneyANgrzJC8jhRWLs+I2rtDmG1pwy+z54nYl8/33b8Cv4EMPb49oH/sDzb2zyhhyu5K5akUBT+5pueAKfddpY9KayGTNfyPPNSsLqfa6+cZzRxkZ06uFiew41c3ouIpakDlIKAPp7NRxhcd2NlN/qpt/uG4FbpexalnrzeHomb6Y9vqaC9ooLIBNlR7qT3XFXZXj0Og4vedGFzRcZ6Esyc/gwdsv4cTZAT7+s10RcZ34hkY52Tk4bTwhnLetL6Gjb5iXwlaDvqFRjp7pZ31pdOMJ4YgYq4XmnnP8IkqxFjtT19CJ0yFsqIh8fUI4ZcG5ClPEFXxDo3zxd4dYV5rDzZd6Q9vXlrjxKyZ1RVoRbRQWQG2Vh6FRP3tO22dpOBsiNUdhoVxelc+/3HgxLxzu4N+eXnhG0oHmmYPM4bzhokKy05Je0/Zi7+lelIJ1UQ4yT+T1y/LZWJHHt/541FbpjWawNRBPyExNiurrFOekk+yUKdNSv/7sEToHhvn8jatxOM6vItd6jfebXeIK2igsgMuW5CESf/MVzKhmni3vuaycD2yu4AcvneBnrzQu6LmC7S1mCjIHSUt28pa1Rfx+f1uoKnVXYzciUG3iSgGM1cLdb1xOu2+YH9edMvW1rczgyBh7TkevPiEcp0MozXVNWtV8sNXHwy+f5N0by1jjfe1FR2F2Gouz02yTlKKNwgLIcaWwcnF23AWb2yIwmzmSfPbNK9m81MMXfntwQVfJ+5p7KXKnkT+H2oub1pVwbnScZ19tB4zOqEsLMqNSNTsTmyo9vG5pPg++cNx2rROixY5T3Yz5VUSH6kxHmefCFtpKKe59Yj/u9GQ+86aLJn2cnYLNphkFEblORA6LyDERuWeS/XeLyKsisldEnheRcrO0LYTaKg87GrvjakkfanFhgZUCGI3q/vrKpfQPj/GnQ2fm/Tz7m3tn7ToKsqEij5KcdB7f1YxSil2N3VGvT5iOu9+4nM6BEX748smYabASW493kuQQasqjnx4MxrzmU52Dr6lf+fXuZraf7Ob/XLdiyqy2tSVuGs4O0HsuckkT0cIUoyAiTuDbwPXAKuA2EVk14bBdQI1Sai3wS+ArZmhbKLWVHkbG/JaYOxwp2nqHcaU4yYqyj3Yu1FZ5yM9MnbLKeCYGhsdoODsw515FDodw0/pi/nK0gx2nuukeHI1KE7zZcklZLlevKOShPzfgi2BWll2pa+hkrddNhknv1TJPBv3DY3QNjADQNzTKvz19iGqvm3fVlE75uLUBd+MBG/RLM2ulsBE4ppRqUEqNYMxgvjH8AKXUn5RSwXVZHcYcZ8uzYUkeDomveoV23xCLs9NMS7mcDU6H8Na1RTx/6My8UlRfbfWhlNHwbq7ctK4Ev4LP//YgELlJa/PlU9cup/fcKN//y4mY6og1A8Nj7G3qNSWeEKTC89oMpAeeO8rZ/mHumxBcnkiwrcpebRRClADhuXRNgW1T8SHgd5PtEJE7RaReROo7OmLfVtidnszFxe64ao7XFsXhOgvhhupiRsb8/OFA+5wfG2yXPV3Po6lYtiiL1SXZ7DndQ0aKk2WFWXN+jkiyusTNm9cs5vsvnqA7cMWaiNQH4glmGoVgrUJj5yCH2/r44csnuXVD6YyJB3kZKXhz020RbDbLKExmQietFReR24Ea4KuT7VdKPaSUqlFK1RQUFERQ4vyprfKw22adEKejrXeIRVEarrMQLinLwZubPi8X0r7mXgqyUimcp7G7KdAkr7o0B+c0V4Rm8alrljMwMsZ3/9wQaykxo64hEE+IYr+jiXhzXYjAyc4B7n1iP1lpSXzmTStm9dhqb44t0lLNMgpNQLjDzQtc8MkWkWuAzwJblFLDJmlbMLWVHkbG/ew0cXpYtPD7FWf6hiwTZA5HRLihupgXj52ls39ub48Dzb55rRKCbKkuJtkZ/QKp2bJsURY3rSvhhy+f4Ezf1G0X4pmtxzupLs3BlWJe7Cst2cni7DR+uq2RbSe6+MybLiIvY/qWKUHWeN00dZ8LxSOsillGYTuwTESWiEgKcCvwZPgBIrIe+C6GQZh/ikkMqKnIxemQuHAhdQ2OMDquLJOOOpEt1cWM+xVP75/9jIFzI+McPdPH6lnWJ0xGYXYaT3/i9Xz0ysp5P0ek+eTVyxgdVzz4wvFYSzGd/uEx9jX3Rq1V9nSUe1yc6RtmTYmbWzeUzfpxa4NxBYu7kEwxCkqpMeAu4BngIPCIUuqAiNwnIlsCh30VyAQeFZHdIvLkFE9nObLSklld4o6LYHO7xWoUJrJicRbLCjN5avfsXUgH23z4FVy8gJUCGFfnZl6VzkRFfgY3X+LlJ3WNtPTE/+zgcLaf7GLc5HhCkAqPMZrzvhsvnpMrcXWgqM3q9Qqm1SkopZ5WSi1XSlUppb4Q2HavUurJwO1rlFKLlFLrAj9bpn9Ga1Fb6WFPU49tml5NhdVqFCYiImypLuaVk12z/iI8sIAgs9X5+NVLUSi+9adjsZZiKnUNnSQ7hUtNqk8I52NXVvHd914659Tk7LRkKvMzLJ+BpCuaI8SmyjxGxxU7bB5XaOs1fPVWXSmAkYUEzHrwzP5mH3kZKRRZ1NAtBG+ui9s2lvHI9tMRbzNuZeqOd7KuNCdq41CnoyI/gzddvHhej13jdWv3UaKwoSKPJIfY3oXU5htCBAqyrJd9FKQiP4Nqr3vWWUj7mo2ZzFaqu4gkf3vVUpwO4Zt/PBprKabQNzTKvmZz6xMixVpvDu2+4dCK3IpooxAhMlKTWOt12745XnuvMYYz2cQxnPPhhupi9jf7aOjon/a44bFxjrT3xaXrKMii7DTeV1vOYzubOD7D+YgH6k9241fEJMi8UNbaIK5g7U++zdhU6WFvU6+tm5W1BaqZrc4N1cWIMONq4XBbH2N+NeeeR3bjY1dWkZbs5IHn4n+1sLWhkxSnI6btRubLxcXZOMTaGUjaKESQ2ioPY37F9pP2ncbWbtFq5oksyk7jsiV5PLmn5TXNySayPzhDYY49j+yGJzOVD25ewlN7WjjYao9hLvOlriF28YSF4kpJYllhlqWDzdooRJCa8jySnWLrEZ1GiwvrxhPC2VJdQkPHwLQTrfa39JKdlkRpXrqJymLDR15fSVZaEl9/9kispUQN39Ao+5t7oz6POZoE22hPdzETS7RRiCDpKU7WlebYtohtaHScnsHYjuGcC9evXkySQ3hqGhdSsF12vAaZw3G7kvnI6yv5w6vtlnZPLITtJ7rwKyPbz66s9brpHBih2aK1JdooRJhNlR72N/dGdNi8WVi9RmEiuRkpXLG8gKf2tOD3X3jVNTru51BrX9zHE8L5wOYKcl3JfO0P8blaqGvoJCXJwSU2jFL5fiUAACAASURBVCcEWes1mudZNdisjUKEqa30MG7TuEK7z/o1ChPZUl1MS+8QOxovrA852t7PyLg/oYxCVloyH7uyiv890mHL9+BMbG3oZH1pDmnJ9osnBFmxOIskh1g2rqCNQoS5pDyXFKfDlnGFNgvNZp4t165aRFqygycnaXsRbJe9kJ5HduR9tRXkZ6Zyf5ytFnrPjXKgxWfL+oRw0pKdXLQ4y7IuPm0UIkxaspN1ZTm2LGJr7w24j2y0UshITeLqlYt4el8rY+P+1+zb39JLZmpSqFdNopCe4uS2jaVsbeiMm3buAK+c6EIpTJvHHE3WBtpoWzHYrI1CFKit9HCgpdcW81jDafMNkZ7sJDvNOk3fZsOW6mI6B0Z4aYIh3tfcy6ri7GknYsUrlQWGIbRqMHM+BOMJ62YYaGMH1nrd9A2NccqCrUm0UYgCtVUe/MrIlLATbb4hFrutNYZzNrzhogKy0pJ4YndzaNvYuJ+Drb64r0+YitJcY0LY6S7rfenMl7qGTi4ty7V1PCFIsMJ+jwVdSNooRIF1pTmkJDlsl5rabtGJazORmuTkuosX84cD7SF3ScPZAYZG/azxJlY8IYg3aBS642Ol0DM4wqut9o8nBLlocRYpSQ5LZiBpoxAF0pKdXFqWa7u4gl1aXEzGlnXF9A+P8adDxnym4IctUVcKhVmppCQ5aOqOj5VCMJ5g5/qEcJKdDlYVZVsyA8k0oyAi14nIYRE5JiL3TLL/ChHZKSJjInKzWbqiRW2Vh4NtPnoGrT16L4hSijO+YVsFmcOprfSQn5kS6oW0v6WX9GQnlQWZMVYWGxwOwZuTTlNXfKwUtjZ0kprkYF2Z/eMJQaq9bvY39zI+SY1NLDHFKIiIE/g2cD2wCrhNRFZNOKwReD/wUzM0RZtNlR6Ugm02iSt0DYwwMu63rVFIcjp4y5oinj90hr6hUQ40+1hVnD2nyVjxhjfPxek4WSnUNXRxaXkuqUn2jycEWePNYXBkfMZOv2Zj1kphI3BMKdWglBoBfg7cGH6AUuqkUmov4J/sCexGdambtGSHbVxIocI1G9UoTGTLumJGxvz8fn8bB1p6E64+YSLe3HSa4iCm0DM4wqE2ny1bZU9HsI32XovFFcwyCiXA6bD7TYFtc0ZE7hSRehGp7+joiIi4aJCa5KSmPM828xVCLS5sulIAuKQsl5KcdB584TgDI+MLnslsd0pzXXQNjNi6lTsYqwSlsHUTvMmoKsjEleK0XBGbWUZhsjX8vBxpSqmHlFI1SqmagoKCBcqKLrVVHg619dE1YP24gh2rmSciItxQXUzD2QEgPmcyz4VgZ1i7u5DqGjpJS3aErqzjBadDWF3stlyw2Syj0ASUht33ArObpWhjgpkS22ywWmjrNcZwFlp4DOds2BKY35yS5GBpYWIGmYOE0lJtHmyua+ikpjwvruIJQdZ43bza4mN03Dpec7OMwnZgmYgsEZEU4FbgSZNeO2as9ebgSnHaol6h3TeEJ8P6YzhnYmVRFssXZbK6ONv2f8tCKc01Vgp2TkvtGhjhUFtf3KSiTmSt183wmJ8j7X2xlhLClH4GSqkxEbkLeAZwAj9QSh0QkfuAeqXUkyKyAXgcyAVuEJF/UUpdbIa+aJHsdFBTYY+4glHNbO9VAhgupO/fsSHWMixBXkYKrhSnrVcKr5wwPjvx0O9oMsLbaF9skZoa05rcKKWeBp6esO3esNvbMdxKccWmyjy+8vvDnO0fJj/Tul+6bb1DeHPjYzpZaZ4r1hIsgYjgzU23dUxh6/FO0pOdrCmJn/qEcMrzXGSlJbG3uZdbYy0mQGKvr00gmEZn9dWCXWYza+ZGaa7L1mmpdQ1d1FTkkpIUn19VDoew1uu2VAZSfJ5pC7GmxE1GitPSRmFodJzuwVFtFOKQ0jwXTV2DlmzRPBOd/cMcbu+Lm35HU7GmJIfDbX2WaXOujUKUSXI62LAkz9JFbB199pu4ppkd3tx0+obHbNfGHc53A4h3o7DW62Z0XHG4zRrBZm0UTKC20sPxjgHOBGoBosnju5rmXDbfZrPZzJrZY+e01LqGTlwpzrirT5jI+cpma7iQtFEwgWDmRF2U+yANDI9x9yN7+PxvXp3T49oCE9f0SiH+CBaw2TEtdevxTmoq8uI+tbgkJ528jBTLtLuI77NtEVYVZZOVmhR1F9Khtj6UgheOdMxpuEqwxYU2CvHH+bkK9jIKZ/uHOXqmP27rE8IREdaUuNlnkcpmbRRMIMnpYOOS6NcrHGz1AaAU/OyVxlk/rq13iLRkB9np9hrDqZkZd3oy2WlJtnMfBT8r8dYEbyqqvW6OtPcxOBL7PlXaKJhEbZWHE2cHQq6aaHCozUd2WhLXrFzEI/WnGRmbXel8cLiO3cZwamZHqQ1baNc1dJKR4mR1gvSvWuPNwa/g1RZfrKVoo2AWm0yoVzjY2seKomxu31TG2f4RnjnQNqvH6RqF+MaOtQp1DV1sWBL/8YQgwWDzHgvEFRLjjFuAlUXZZKdFL67g9ysOtfpYuTiLK5YVUJqXzo/rTs3qsUaLC20U4pXSvHSauu1Tq3Cmb4hjZ/rjPhU1nEXZaSzKTmWfBTKQtFEwCadDuKzSE7XmeKe7BxkYGWdlUTYOh/DujeVsO9HFsTPT5z4rpWj3DesgcxzjzXUxNOqno3841lJmxbaGxKhPmMiakhxLtNHWRsFEais9NHYN0twT+aX8wVbjy39lkTFt7J01XpKdwk+2TR9w7h4cZWTMT6E2CnHL+bRUe7iQtjZ0kpmalHCT89Z63TR0DOAbim2hoTYKJhKKK0TBhXSw1YdDYPmiLAA8malcv7qIX+1o4tzI1OXzOh01/ikNFbDZI9hc19DJhopckhIknhAkGFfYH+PVQmKd9RizYnEWua7kqLiQDrb6qMjPID3l/CCS91xWhm9ojKf2Tj3P6PzENet2cNUsjJJc+6wUzviGaOgYiNtW2dMRnBS4L8bBZtOMgohcJyKHReSYiNwzyf5UEflFYP82EakwS5tZOBzCZUs8UclAOtTWF3IdBdm4JI9lhZn8ZJqAc3uv/Wcza6bHlZJEfmaKLVYKwQumRIsngLG6L8lJj3llsylGQUScwLeB64FVwG0ismrCYR8CupVSS4GvA182Q5vZbKrMo6n7XEQ/oH1DozR2DbJqglEQEd5zWRl7mnqnvPoIrhQKs7RRiGe8NklLrWvoIis1yTIDZ8ymutTN3ubYZiCZtVLYCBxTSjUopUaAnwM3TjjmRuDhwO1fAldLHFZT1VblA0TUhRTsrrhicdYF+95+qZf0ZCc/2Tb5aqHdN0R+Zkrc9qvXGNilgK2uoZONS/JwOuLuoz8r1pTkcLrrHN0DIzHTYNY3QQlwOux+U2DbpMcopcaAXuCCNaSI3Cki9SJS39HRESW50WP5okzyMlIi6kIKtreY6D4CyE5LZkt1MU/sbpk0q6GtVxeuJQLe3HRaes4x7rdurUJb7xAnzg4kpOsoSKhjagyDzWYZhcnM/sR352yOQSn1kFKqRilVU1BQEBFxZiIibKrMo+54Z8SKiQ629eFOT6ZoigK02zeVc250nF/var5gX5uuUUgISnNdjI6rULaZFQn1O0rAIHOQ1aFgc+xcSGYZhSagNOy+F5iYEhM6RkSSADcQ3V7TMaK20kNL7xCNEYorHGz1sbIoa8reRWu8btZ63fy47tQFhqjdN6TnKCQAwVoFKweb6xo6yU5LmnTFmyi405NZkp8R02CzWUZhO7BMRJaISApwK/DkhGOeBO4I3L4Z+KOyS13+HAleCUWi5YXfb0xsWrF4+g/S7ZeVc6S9n/pT3aFtw2PjdA2M6JVCAnC+hbZ1g81GPMGTsPGEIGtK3PFvFAIxgruAZ4CDwCNKqQMicp+IbAkc9n3AIyLHgLuBC9JW44WqgkzyM1MjEmw+1TXI4Mj4BZlHE3lrdRFZaUmv6Yd0xme0PViUrWsU4p3inDRErLtSaO09x8nOwYSYnzATa71u2nxDpkxqnAzTGugrpZ4Gnp6w7d6w20PALWbpiSUiQm2Vh62BuMJCkqwOTRNkDseVksQ7LvHy022N3PvWYTyZqSH/sg40xz+pSU4WZ6dZNi21LoHrEyay1psDwL7mXq6OwWdT5yHGiMurPJzpG+b4HOcpTyTY3mLZoswZj33PZWWMjPt5dEcTEF7NrI1CIlCaa9201K3HO3GnJ8+44k0ELi7OxiGxa6OtjUKM2ByoV3jp2MJcSK+29lFZkElasnPGY5ctymLjkjx+uq0Rv1/p2cwJhjc3nSaLuo/qGrrYuCQPR4LHEwAyUpNYWpgZswwkbRRiRJnHhTc3nZeOnV3Q8xxq880pW+P2TeU0dg3y4rGztPuGSE1y4E5PXpAGjT3w5rlo8w3NeiKfWRxt76Oxa5DNCZyKOpE1JTnsa+6NyQwMbRRiyOaqfOoaOuddUOQbGqWp+xwriy6sZJ6KN128CE9GCj+uO2XUKLj1GM5EoTQ3Hb8ygrpW4tEdTSQ5hLdWF8daimWoLnVztn+EliiO750KbRRiyOVLPfiGxubdKvdQcIbCDOmo4aQmOXnnhlKeO9jOvqYeHWROIEJpqV3WMQpj434e29nMVSsKyc/UWXBB1sSwiE0bhRhyeTCucHx+LqTp2ltMx7s3lqGAk52DOp6QQIQK2CwUbP7fIx2c7R/mlku9sZZiKVYWZZPkkJjUK2ijEEMKslK5aFEWL88z2HyozUeuK3nOdQaleS6uXG60CNGZR4lDkTudJIfQZCGj8Gh9E56MFK5aURhrKZYiLdnJRYuztFFIRC5f6mH7yS6GRqeejjYVr7YaMxTmExN4z2XlABRm6SV7ouB0CMU56ZZxH3UNjPD8oXZuWl9CcoJNWZsNa71u9jb1mB5s1v+JGLO5Kp/hMT87G7tnPjiMcb/icJtvxvYWU/FXKwq55/oV3KCDewmFNzfdMu6jX+9qZnRccUuNdh1NxpqSHHxDYxHrkTZbtFGIMZdVGr3j5+pCOtk5wNCof06ZR+E4HcLHrqzSgeYEo9RCw3Z+uaOJNSXueV/YxDvBNtpmF7FpoxBjstKSWet1zznYHMo80hWgmjlQmpdOR9/wvNyVkeRASy+vtvr0KmEali/KIiXJYXoGkjYKFmBzVT57m3rpm2QIzlQcbPXhdAhLC2dub6HRBAmmpcY62PxofRMpTgdbtPtySlKSHKwsyjY92KyNggW4fKmHcb9iW8Psx0ccbPVRVZAxq/YWGk2Q83MVYudCGhnz88TuZq5dtYgcV0rMdNiBaq+b/c29pk7M00bBAlxSlktqkmNOLiRjsI52HWnmRqkFVgp/PNRO9+AoN2vX0YysKXEzMDLOibMLa5w5F6JuFEQkT0SeFZGjgd+5Uxz3exHpEZHfRFuT1UhLdrKhIm/WwebewVFaeoe0UdDMmYKsVFKTHDEdtvNofROLslO5Ypn9xumaTbCNtpkuJDNWCvcAzyullgHPM/XwnK8C7zVBjyWprfJwuL2Pjr7hGY892GZUMq9YPL/MI03iIiKU5KbHbNjOmb4hXjjSwdvWexN+wtpsWFqYSXqyM+6Mwo3Aw4HbDwM3TXaQUup5oM8EPZZk81Kj5cXLs3AhBdtb6N7zmvkQy7TUx3c2M+7XtQmzxekQVpdks9fEDCQzjMIipVQrQOD3gurZReROEakXkfqOjo6ICLQCa0rcZKUlzWpu86HWPjwZKRToamTNPCjNi00Bm1KKX+5o4pKyHKoKdNbcbFlTksOBFh9j4+a0PI+IURCR50Rk/yQ/N0bi+cNRSj2klKpRStUUFMSPT9LpEDZVemYVbD4YmKGgW15r5oM310XP4OicUqAjwZ6mXo6e6eeWmlJTX9fuVJe6GR7zc6TdnGBzRIyCUuoapdTqSX6eANpFpAgg8PtMJF4zHtlc5eF017lp/b1j434Ot/XpeIJm3pTGqIX2o/WnSUt28Ja1Raa+rt0JtdFuNseFZIb76EngjsDtO4AnTHhNWxKMK0w3je1k5wDDY36deaSZN8FaBTPTUodGx3lyTwvXXbyY7DQ96W8uVHgyyEpNMi3YbIZR+BJwrYgcBa4N3EdEakTke8GDROQvwKPA1SLSJCJvMkGbpVhamElhViovTRNXOKjbW2gWSGjYjonB5mcOtNE3NKZdR/PA4RDWeN2mGYWkaL+AUqoTuHqS7fXAh8Puvz7aWqyOiHB5lYcXj51FKTVpzOBgq48kh1BVmBEDhZp4INeVTEaK09S01F/uaKIkJ53aSj2HeT6s8br5wYsnGB4bJzUpul0MdEWzxbh8aT5n+0c43D55du7BVh9LCzOj/sbQxC8iQmmeeWmpLT3nePHYWd5xqReHrk2YF2tLchgdVxxui37WvjYKFuN8XGFyF9LBwGAdjWYheHNdpsUUHtvZhFJw8yW6NmG+mNlGWxsFi1GSk06Fx8XLkwSbuwdGaPMNzXuGgkYTxBuoao72VK9gbcJlS/Io87ii+lrxjDc3nVxXsilttLVRsCCXL81n24muC4pVzre30CsFzcIozXMxMDJO92B0axXqT3VzsnNQB5gXiIiwxptjSrBZGwULsrkqn/7hsQuWijrzSBMpSnPNSUt9tP40GSlO3rxmcVRfJxGo9ro50t7HuZHoDkjSRsGC1FYZGRoTXUgHW33kZ6bq9haaBeM1oYBtcGSM3+5t5c1rinClRD3RMe5ZU+LGr+DV1uiuFrRRsCB5GSmsKsq+oOXFoTafjidoIkJo2E4UVwpP72tjYGRcu44iRLCN9p7T2igkJJuXeth5qie0VBwbN3qfaNeRJhJkpSWT40qOWq3CyJifn2w7RYXHxYaKSUeoaObIYncahVmp7GvWRiEhuXxpPiPjfupPGSM6G84OMDLm1ysFTcSIVgvt3sFR3v/fr7CrsYePXVmlGzdGkLVed9TbaGujYFE2VuSR5JBQvUJwhoJeKWgihTc38i20GzsHefuDL7H9ZBdfu6WaWzeWRfT5E501JTk0nB2IaodbbRQsSkZqEutKc0JDdw629pHsFCrzdR96TWQIVjX7IzQUfsepLm76zkuc7R/hRx+6jHdcqovVIs3aUjdKwf5mX9ReQxsFC3P50nz2NffSOzgaaG+RRUqS/pdpIkNpbjojY37O9s88AnYmntzTwm3/tY3stCQe/5vL2aR7HEUFM9po628YC7O5yoNSsLWhk4OtOvNIE1nOd0udvwtJKcV/PH+UT/xsF+u8OTz+N5up1FPVokZ+ZiolOelRbXehjYKFWV+WS3qyk9/sbeFM37CeyayJKKG01HnWKgyPjfP3j+7ha88e4W3rS/jRhzeSm5ESSYmaSVjrdbNPG4XEJCXJwYYlefxufxug21toIsv5Ara5rxR6Bkd47/df4bGdzXzqmuXc/85q3bnXJNZ43TR2DdIzOBKV54+6URCRPBF5VkSOBn5fkLQsIutEZKuIHBCRvSLyrmjrsgubqzyMBwKB2n2kiSRpyU4KslLnnJZ64uwAb/vOy+xu7OEbt67jk9cs02mnJlIdKGKLVh8kM1YK9wDPK6WWAc8H7k9kEHifUupi4DrgARHJMUGb5Qm20i7MSsWTqdtbaCLLXNNStzV08rbvvETP4Ag//chl3LiuJIrqNJOxujgYbLavUbgReDhw+2HgpokHKKWOKKWOBm63AGeAAhO0WZ5VRdnkupJ1fYImKpTmumZlFMbG/Tzw3BHe/b1t5LlSePxvNlNTkWeCQs1E3K5k3n5JCSU56VF5fjO6VC1SSrUCKKVaRaRwuoNFZCOQAhyfYv+dwJ0AZWXxXxjjcAgP3n4peTqAp4kCpXnpPL2vlbFxP0nOya8RT3cN8qlf7Kb+VDdvW1/CfTdeTFZasslKNeHc/851UXvuiBgFEXkOmKw37mfn+DxFwI+AO5RS/smOUUo9BDwEUFNTE90JIRZB53xrooU318WYX9HmGwoFnsN5ck8Ln31sHwp44F3ruGm9dhfFOxExCkqpa6baJyLtIlIUWCUUYbiGJjsuG/gt8DmlVF0kdGk0mukpDWuhHW4U+ofHuPeJ/Ty2s5lLynJ44F3r9eS0BMGMmMKTwB2B23cAT0w8QERSgMeB/1FKPWqCJo1Gw+QttHc1dvPmb/yFX+9q5hNXL+ORj9Zqg5BAmGEUvgRcKyJHgWsD9xGRGhH5XuCYdwJXAO8Xkd2Bn+g5zTQaDQBF7nQcAk3d5xj3K771x6Pc/J9bGfcrfvHRWu6+dvmUsQZNfCLRHtwdTWpqalR9fX2sZWg0tubyLz5PVWEmw2N+XjnRxQ3VxfzrTatxp+tgcrwiIjuUUjWT7dMz8jSaBMeb5+IvR8+SkeLka7dU8/ZLSnQxWgKjjYJGk+Bcd/FiUpMcfP7G1VTkZ8RajibGaKOg0SQ4H3zdEj74uiWxlqGxCDqCpNFoNJoQ2ihoNBqNJoQ2ChqNRqMJoY2CRqPRaEJoo6DRaDSaENooaDQajSaENgoajUajCWHrNhci0gGcmufD84GzEZQTbeyk105awV567aQV7KXXTlphYXrLlVKTDjKztVFYCCJSP1XvDytiJ7120gr20msnrWAvvXbSCtHTq91HGo1GowmhjYJGo9FoQiSyUXgo1gLmiJ302kkr2EuvnbSCvfTaSStESW/CxhQ0Go1GcyGJvFLQaDQazQS0UdBoNBpNCG0UNBoLInr0mSZGaKNgEUTk7SKSG2sds0VEssJuW/oLTETKRSQ1cNvSWgFE5N+AdwVuW1qviDgCvy2tM4iIfEBECmOtYzaIyCIRSQrcNu38xqVREJEPi8gjIvL6WGuZCRG5XUTqgNcBQ7HWMxMi8k4ROQB8SUS+AqAsmq0gIu8Rkb3AV4BfgnW1Qui98DzwQeCjYF29IvLBgNZ3xlrLbBCR94rIi8BlwGCs9UyHiLxDRI4AXwa+Cya/D5RScfUDvAl4FfgV8H+A3MB2ibW2MI2CYZA/AIwDl8Va0yx1XwT8BdgYuP8i8MlY65ri/L4b+DOwObDtDHCN1d4LAT2pwGeAP2F8aZUA/wOUxFrbFHovA3YBDwL/ARRb8byG6d0C+IPv24nvlVjrm6CnBPgjUBO4/yhwb/B7zIyfuFgpiEj4tPF64BrgW4AXuBKsc8UlIhnKwA9sA34BDIuIQ0TuEJGVMZb4Giac2xJgL7AvcP+/gH8SkfWmC5uGwP/6WaXUFUqpl0QkH+NcnwvbbxmUUsPAL5RSVymltgFLMM61L7bKziMirrC724G3A98EhoFbwFrndYLeZwI/OYF9HxeRzWANzRO0ZgNtQEfg/g+AvwWuFBGnGXpsbxRE5P8CL4rIZ0XkWqVUp1KqBfhfoBmoEZGKwLEx9XuGaf2ciPyVUupV4FngN8AeoBb4gYh8MXB8TP8/E/TWAieBCuDawLl0A8eBtwWOj5leEblPRO4M+mAJNAoTkeXAb4ER4G4R+aKIZMZKZ5CJepVSjcF9SqkXgUICFzQWeN/+A/D7wPvgGqWUXyl1Qil1EHgFuFhEqgPHxvw7ZYLeawNG95vAUwF3YiXwZRF5INbvhQlaX4fxvj0HvF1E0jEuEPYAl2LS93XM/4ELQUTeDlwHvBfDAHw18CVA4Er8OSALY+UQ06uCCVqbgPtFpApjpfA94Cal1McC+98vIsWBv8EKepuB/wQGgB8BNwIvA8uBO4F3ikhOLPSKSJaIfDmg4zaMD1H4/7oZuE4p9Q7gLuB6YKnZOoNMpTdsf/Az+SvgYojd+1ZEikXkcaAawxU7DNwZWHkF2Qa0cn61EMv37GR6PyIi+Uqp3wGfBd6tlPoUcCvwegwDYRWtnwD6gJ8BVcDTQA3waYzPXJ4Z2pJmPsTS5ABPKKX2A/tFZAlGYOYqAKXUDhFZDZSLyPuBxUqpL1lEayXwkFLqahH5YuBqBqXUMRF5GSgHWmKkdTK9VcDDSqnrROQxoEopdTDwJfYC4BARicEX2BDwFPCPGC7Dd4vIV5RSQVfRAIYxQynVKiIHMdyKu03WOVu9wS/VdGAMQEScSqnxGGj1Ab9TSj0U0NEPrAZcwf+1UuqUiDwHbBGRuzFWj19VSvVbRO8aIAPjCvz+4PlVSjWJyFHOu0StoLUayFNKPQc8JyIVSqmTgf07gQwzPmO2XikAaRhZOwAopf4ZqBCRG8KO2QXcAcTKGASZqPVeoFJE3ho0CCKSLiIPYFwRvBobmSEm6v0ccJGI3KiUGgkYhEyMFYRLKdUViytapdQosCPwYf828AaMD9drEJFMEfkahvtrp5kaw5lJb5jf+C8YWUjEwiAEvnz6Ma5ag7QAK4FBpZQKc2s1AddiGLqBWBiEafSu4PxFgT9wbJaI3A+UYXw/WEXrcgw3JwBKqZMiki8iD2J8JzSa8RmzpVEIvhmVUt8BNojINWG77wNuDxyXAnwd4wNWGYtVwiy0vjdw3FUY7i6Atyilek0VGmAWet8dOG4JxhWvEEifjBVKqXOBD9oBjP/1B0QkD4y/R0SuAH4POIGrAzGnmDGd3jADcAgj7dcZi5hC8MtHKdUXtnk50KyUOht+DIZx24MxuOUrpgoNMBu9AGIkRfwG471wjVKqzVShzElrDvATjO/pW5VSY2YJtOwPcBNGylvehO0CpAZufwTYH7bvGuDznG/2l21xrf8auF2KiSmIEdCbBngsoNURuO0M/M7DCCy/AXgLht84CSiyyLmdTu+bgWuDx1pQ6y3AA4HbbwAuD9zOsOi5Ddd7FbAu8F5YZAOtwbRvt1nnNqTP7Bec5ckUjJS3gxhL03eEnUQJO64s8PvHwDeAmzGuCL9kI61fttm5NU3vXLQCWWH3/x9GBserwJU20nsAeIMFtWYHbn8RI2b3nxg1FTUWPbeT6X3BLL12O7cX6I/VC8/ixK4DPIET+njwSyqwzwn8C3AYWAsUYVjlp4B7tNb40TtLrfsx0jcFuBxoAP7R6Xc5QQAABt1JREFUwufWEnpnqfVAQGsK8BJGCnJMChbtpNdOWi/QHmsBYSfqDgJL58D9pLDbjwB/ByQH7i8BPseEKr/wx2it9tS7UK3AIsKuwrXeiGp9z8T3hdZrP60z/i0xFwC5GH1pWjFSw4I+Ngfn4wKvA54H1k/yeKfWan+9EdBqmpG1m94IaE2x2bk1Ta+dtM72J+bZR0qpbuAPGKluOzD6fAT3qcDvFzHyyq8XkRUi8lEIpXaZlq5nJ6120xsBreZkZthQbwS0jlz4rFqv3bTOmlhaJM5b0rTA7w0YfXXKA/ednA/QlAL9QDvwT+GP11rtrddOWu2m105a7abXTlrn8mPqjOZgZeZ0VXlitGMuVkrdHrZtEcaQagE+qpRq1Vrtq9dOWu2m105a7abXTloXhEkWtRajo+anCasbIMyShm0rA+ow+r4UYARlnIRF77VWe+q1k1a76bWTVrvptZPWSPxEPaYQqCb9FkaP8GLgH0XkjWBUbyql/IH2DpmBbY0YKVz7MKo9cwPHNU7+Comp1W567aTVbnrtpNVueu2kNWKYYGXvBr4ZuJ2H0R/8vwlUFWK0TngMWBu4fxtwCmNaVrKZFtJOWu2m105a7abXTlrtptdOWiP1E/EuqSKyCehSSh0JbDoMVIvRCrpFjG6A+cCNIvICRhvjzyiljgeOP4FR1Xki0trsrNVueu2k1W567aTVbnrtpDVaRCzQLOebN12BMVv0AaVUvxjzDT6OMcqxDyN/92WMjK1/D3u8ae2B7aTVbnrtpNVueu2k1W567aQ12kTSKJRglHT7CMzyVUo9HdiXAmzGWHL9XESuB+5SSr0lsN+hTBzOYSetdtNrJ61202snrXbTayet0WZBRkFE3ofhP9ullPKJSBqGJf0MRvrVQ2qSNsUi8jmgRyn1rXm/eBxrtZteO2m1m147abWbXjtpNZM5Zx+JQZGI/Amj38d7gAfFGHk3pJQaxJgLkAv81YTHvk5EdmC0M/7NwuXHj1a76bWTVrvptZNWu+m1k9ZYMSejIIbfTGHMPW5WSl0N/A3QhVGcAYBS6iWMIe8rRMQtIhmBXQ0Y1XxvUoExc9HCTlrtptdOWu2m105a7abXTlpjyazcRyKShJF65cQYJp0N3KyUuiOwXzDGyd2qlPrfwLZM4F8xWgOXA5cqpZqi8UfYVavd9NpJq9302kmr3fTaSasVmHGlICJXYjR6ygWOYUw1GwWuEpGNEGr8dB/GsJAgb8GwwnuANSb9822j1W567aTVbnrtpNVueu2k1TKomYs3Xg+8N+z+d4C/Bt6PMYAcDOOyGKNveEVg243AFTM9fyR/7KTVbnrtpNVueu2k1W567aTVKj+zOakuIJXzfcLfA3wxcHs38PHA7RrgZzH9Y2yk1W567aTVbnrtpNVueu2k1So/M7qPlFKDSqlhdb4w41qgI3D7A8BKEfkN8DNgJ4R8dKZjJ61gL7120gr20msnrWAvvXbSahVm3eZCRJyAwhgf+GRgcx/wf4HVwAmlVDOcHy4RK+ykFeyl105awV567aQV7KXXTlpjzVxSUv1AMnAWWBuwrv8E+JVSLwZPqEWwk1awl147aQV76bWTVrCXXjtpjS1z8TUBmzBO7ovAh2Lt+4oXrXbTayetdtNrJ61202snrbH8mVObCxHxAu8F7ldKDc/ZApmInbSCvfTaSSvYS6+dtIK99NpJaywxdRynRqPRaKxN1CevaTQajcY+aKOg0Wg0mhDaKGg0Go0mhDYKGo1GowmhjYJGo9FoQmijoNHMAREZF5HdInJARPaIyN0iMu3nSEQqROTdZmnUaBaCNgoazdw4p5Rap5S6GKOPzpuBf57hMRWANgoaW6DrFDSaOSAi/UqpzLD7lcB2IB9jGMuPgOCkrruUUi+LSB2wEjgBPAx8E/gS8AaMDp7fVkp917Q/QqOZBm0UNJo5MNEoBLZ1AyswGqz5lVJDIrIMoxVzjYi8Afi0UuqtgePvBAqVUv8qIqnAS8AtSqkTpv4xGs0kzLpLqkajmZJgq+Vk4Fsisg4YB5ZPcfwbMZqy3Ry47waWYawkNJqYoo2CRrMAAu6jceAMRmyhHajGiNcNTfUwjOEuz5giUqOZAzrQrNHMExEpAP4T+JYy/LBuoFUp5cdovOYMHNoHZIU99Bngr0UkOfA8y0UkA43GAuiVgkYzN9JFZDeGq2gMI7B8f2Dfd4BficgtwJ+AgcD2vcCYiOwBfgh8AyMjaWdgylcHcJNZf4BGMx060KzRaDSaENp9pNFoNJoQ2ihoNBqNJoQ2ChqNRqMJoY2CRqPRaEJoo6DRaDSaENooaDQajSaENgoajUajCfH/AfirLDZ8/z7vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['PercentChange'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>2.250714</td>\n",
       "      <td>2.275714</td>\n",
       "      <td>2.168571</td>\n",
       "      <td>2.193571</td>\n",
       "      <td>1.966391</td>\n",
       "      <td>97230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>2.193571</td>\n",
       "      <td>2.213571</td>\n",
       "      <td>2.177857</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.972154</td>\n",
       "      <td>79195200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open      High       Low     Close  Adj Close    Volume\n",
       "Date                                                                   \n",
       "2004-08-19  2.250714  2.275714  2.168571  2.193571   1.966391  97230000\n",
       "2004-08-20  2.193571  2.213571  2.177857  2.200000   1.972154  79195200"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl = pd.read_csv('../data/yahoofinance-AAPL-20040819-20180120.csv',index_col=0,parse_dates=True)\n",
    "goog = pd.read_csv('../data/yahoofinance-GOOG-20040819-20180120.csv',index_col=0,parse_dates=True)\n",
    "aapl.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt1 = '2016-03-01'\n",
    "dt2 = '2016-07-01'\n",
    "aapl = aapl.loc[dt1:dt2,:].drop(['Adj Close','Volume'],axis=1)\n",
    "goog = goog.loc[dt1:dt2,:].drop(['Adj Close','Volume'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "aapl['PercentChg'] = 100.0*(aapl['Close']/aapl['Open'] - 1.0)\n",
    "goog['PercentChg'] = 100.0*(goog['Close']/goog['Open'] - 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>PercentChg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-03-01</th>\n",
       "      <td>97.650002</td>\n",
       "      <td>100.769997</td>\n",
       "      <td>97.419998</td>\n",
       "      <td>100.529999</td>\n",
       "      <td>2.949306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02</th>\n",
       "      <td>100.510002</td>\n",
       "      <td>100.889999</td>\n",
       "      <td>99.639999</td>\n",
       "      <td>100.750000</td>\n",
       "      <td>0.238780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-03</th>\n",
       "      <td>100.580002</td>\n",
       "      <td>101.709999</td>\n",
       "      <td>100.449997</td>\n",
       "      <td>101.500000</td>\n",
       "      <td>0.914693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-04</th>\n",
       "      <td>102.370003</td>\n",
       "      <td>103.750000</td>\n",
       "      <td>101.370003</td>\n",
       "      <td>103.010002</td>\n",
       "      <td>0.625182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-07</th>\n",
       "      <td>102.389999</td>\n",
       "      <td>102.830002</td>\n",
       "      <td>100.959999</td>\n",
       "      <td>101.870003</td>\n",
       "      <td>-0.507858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-08</th>\n",
       "      <td>100.779999</td>\n",
       "      <td>101.760002</td>\n",
       "      <td>100.400002</td>\n",
       "      <td>101.029999</td>\n",
       "      <td>0.248065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-09</th>\n",
       "      <td>101.309998</td>\n",
       "      <td>101.580002</td>\n",
       "      <td>100.269997</td>\n",
       "      <td>101.120003</td>\n",
       "      <td>-0.187538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-10</th>\n",
       "      <td>101.410004</td>\n",
       "      <td>102.239998</td>\n",
       "      <td>100.150002</td>\n",
       "      <td>101.169998</td>\n",
       "      <td>-0.236669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-11</th>\n",
       "      <td>102.239998</td>\n",
       "      <td>102.279999</td>\n",
       "      <td>101.500000</td>\n",
       "      <td>102.260002</td>\n",
       "      <td>0.019566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-14</th>\n",
       "      <td>101.910004</td>\n",
       "      <td>102.910004</td>\n",
       "      <td>101.779999</td>\n",
       "      <td>102.519997</td>\n",
       "      <td>0.598560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-15</th>\n",
       "      <td>103.959999</td>\n",
       "      <td>105.180000</td>\n",
       "      <td>103.849998</td>\n",
       "      <td>104.580002</td>\n",
       "      <td>0.596386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-16</th>\n",
       "      <td>104.610001</td>\n",
       "      <td>106.309998</td>\n",
       "      <td>104.589996</td>\n",
       "      <td>105.970001</td>\n",
       "      <td>1.300067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close  PercentChg\n",
       "Date                                                                  \n",
       "2016-03-01   97.650002  100.769997   97.419998  100.529999    2.949306\n",
       "2016-03-02  100.510002  100.889999   99.639999  100.750000    0.238780\n",
       "2016-03-03  100.580002  101.709999  100.449997  101.500000    0.914693\n",
       "2016-03-04  102.370003  103.750000  101.370003  103.010002    0.625182\n",
       "2016-03-07  102.389999  102.830002  100.959999  101.870003   -0.507858\n",
       "2016-03-08  100.779999  101.760002  100.400002  101.029999    0.248065\n",
       "2016-03-09  101.309998  101.580002  100.269997  101.120003   -0.187538\n",
       "2016-03-10  101.410004  102.239998  100.150002  101.169998   -0.236669\n",
       "2016-03-11  102.239998  102.279999  101.500000  102.260002    0.019566\n",
       "2016-03-14  101.910004  102.910004  101.779999  102.519997    0.598560\n",
       "2016-03-15  103.959999  105.180000  103.849998  104.580002    0.596386\n",
       "2016-03-16  104.610001  106.309998  104.589996  105.970001    1.300067"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>PercentChg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-03-01</th>\n",
       "      <td>703.619995</td>\n",
       "      <td>718.809998</td>\n",
       "      <td>699.770020</td>\n",
       "      <td>718.809998</td>\n",
       "      <td>2.158836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02</th>\n",
       "      <td>719.000000</td>\n",
       "      <td>720.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>718.849976</td>\n",
       "      <td>-0.020866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-03</th>\n",
       "      <td>718.679993</td>\n",
       "      <td>719.450012</td>\n",
       "      <td>706.020020</td>\n",
       "      <td>712.419983</td>\n",
       "      <td>-0.871043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-04</th>\n",
       "      <td>714.989990</td>\n",
       "      <td>716.489990</td>\n",
       "      <td>706.020020</td>\n",
       "      <td>710.890015</td>\n",
       "      <td>-0.573431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-07</th>\n",
       "      <td>706.900024</td>\n",
       "      <td>708.091003</td>\n",
       "      <td>686.900024</td>\n",
       "      <td>695.159973</td>\n",
       "      <td>-1.660780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close  PercentChg\n",
       "Date                                                                  \n",
       "2016-03-01  703.619995  718.809998  699.770020  718.809998    2.158836\n",
       "2016-03-02  719.000000  720.000000  712.000000  718.849976   -0.020866\n",
       "2016-03-03  718.679993  719.450012  706.020020  712.419983   -0.871043\n",
       "2016-03-04  714.989990  716.489990  706.020020  710.890015   -0.573431\n",
       "2016-03-07  706.900024  708.091003  686.900024  695.159973   -1.660780"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl.head(12)\n",
    "goog.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([aapl['PercentChg'],goog['PercentChg']],keys=['AAPL','GOOG'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-03-01</th>\n",
       "      <td>2.949306</td>\n",
       "      <td>2.158836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-02</th>\n",
       "      <td>0.238780</td>\n",
       "      <td>-0.020866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-03</th>\n",
       "      <td>0.914693</td>\n",
       "      <td>-0.871043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-04</th>\n",
       "      <td>0.625182</td>\n",
       "      <td>-0.573431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-07</th>\n",
       "      <td>-0.507858</td>\n",
       "      <td>-1.660780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL      GOOG\n",
       "Date                          \n",
       "2016-03-01  2.949306  2.158836\n",
       "2016-03-02  0.238780 -0.020866\n",
       "2016-03-03  0.914693 -0.871043\n",
       "2016-03-04  0.625182 -0.573431\n",
       "2016-03-07 -0.507858 -1.660780"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7facd2cb2f90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(figsize=(10,8),grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6251831591286505"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100.*((103.01/102.37)-1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-14-a6774c8535dd>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-14-a6774c8535dd>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    STOP HERE\u001b[0m\n\u001b[0m            ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "STOP HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mplfinance as mpf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "segments=[\n",
    "    [\n",
    "        ('2019-11-8',3095.),('2019-11-15',3120.)#,('2019-11-17',3128.),('2019-11-22',3100.)\n",
    "    ],\n",
    "    [\n",
    "        ('2019-11-17',3128.),('2019-11-22',3100.)\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# mpf.plot(daily,type='renko',show_nontrading=True)\n",
    "# mpf.plot(daily,type='renko',show_nontrading=True)\n",
    "# mpf.plot(daily,type='renko',lines=segments,figscale=1.25,show_nontrading=False)\n",
    "# mpf.plot(daily,type='renko',lines=segments,figscale=1.25,show_nontrading=True)\n",
    "# mpf.plot(daily,type='candle',lines=segments,figscale=1.25,show_nontrading=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#mpf.plot(daily,type='renko',lines=segments,figscale=1.25,renko_params=dict(brick_size=7))\n",
    "mpf.plot(daily,type='renko',lines=segments,figscale=1.25,renko_params=dict(brick_size='atr',atr_length=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "idf = pd.read_csv('../data/SP500_NOV2019_IDay.csv',index_col=0,parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "isegs=[\n",
    "    [\n",
    "        ('2019-11-5 12:50',3080.),('2019-11-5 15:30',3070.),('2019-11-7 14:30',3090.)\n",
    "    ],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#mpf.plot(idf,lines=isegs,figscale=1.25)\n",
    "#mpf.plot(idf,lines=isegs,figscale=1.25,show_nontrading=True)\n",
    "mpf.plot(idf,lines=isegs,figscale=1.25,hlines=[3070,3090])\n",
    "#mpf.plot(idf,lines=isegs,figscale=1.25,show_nontrading=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/yahoofinance-SPY-20080101-20180101.csv',index_col=0,parse_dates=True)\n",
    "df.shape\n",
    "df.head(2)\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "segs =[\n",
    "    [\n",
    "        ('2016-11-8',150.),('2017-11-15',175.)#,('2019-11-17',3128.),('2019-11-22',3100.)\n",
    "    ],\n",
    "    [\n",
    "        ('2011-11-17',200.),('2013-11-22',200.)\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpf.plot(df,lines=segs,figscale=1.25,type='renko',renko_params=dict(brick_size=3.005,atr_length=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#STOP HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mpf.plot(daily,type='candle',volume=True,figscale=0.9)\n",
    "#mpf.plot(daily,type='candle',volume=True,figscale=0.9,show_nontrading=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mpf.plot(daily,type='candle',volume=True,\n",
    "#         title='\\nS&P 500, Nov 2019',\n",
    "#         ylabel='OHLC Candles',\n",
    "#         ylabel_lower='Shares\\nTraded')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mpf.plot(daily, type='line', linecolor='#00ff00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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